Diagnostic Classifications of Pulse Signal Waveform Data

ABSTRACT

Diagnostic classifications of pulse signal waveform data is provided. In one example, diagnosis or prediction of a disease may be performed by analyzing pulse signal waveform data, and comparing aspects of the pulse signal waveform data with a morphology pattern that has been found to indicate a subject is suffering from a specific disease. A pulse signal can be captured via wrist electrodes using bio-electric sensors based on an impedance plethysmographic principle, for example, and processed using feature extraction for diagnosis and assessment of the subject&#39;s proneness to a disease. Analysis of the pulse signal and pulse morphology patterns provides an in-vitro, non-invasive, and low-cost method for diagnosing diseases.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to a corresponding patentapplication filed in India and having application number 1542/MUM/2010,filed on May 14, 2010, the entire contents of which, are hereinincorporated by reference.

BACKGROUND

A pulsating sensation of blood vessels (generally arteries) due toejection of blood by a heart (or machine) is known as a pulse. The Pulseis an abrupt expansion of an artery resulting from a sudden ejection ofblood and transmission of the blood throughout an arterial system. Aleft ventricle of the heart contracts and ejects blood into the aorta,which carries the blood bolus through branches of peripheral arteries tovarious organs and tissues. The arterial pulse perceived by a clinicianis the pressure pulse in a large, accessible artery. Generally, a pulsecan be examined at sites such as radial, carotid, subclavian, brachial,femoral popliteal, posterior tibial, dorsalis pedis etc., and inclinical practice, a common pulse examination site is a radial artery.

Pulse is related to various events of left ventricle, aorta, andperipheral arteries, and hence the pulse reflects a status of relatedorgans. A common application of pulse'examination is a measurement ofheart beats as each pulsation is a result of a ventricular systole. Anabsence of a pulse may suggest occlusion by a thrombus or dissection,and a diminished pulse may relate to vascular pathology or impairment ofa cardiac function, for example. A low volume and amplitude(hypokinetic) pulse is found in low cardiac output while a high volumeand amplitude (hyperkinetic) pulse is a sign of anxiety, exercise, feverhyperthyroidism, and anaemia, for example.

Pulse is also studied with other functions and events (e.g., inspirationand expiration related changes as pulsus paradoxus). Pulse changesaccording to various factors such as age, time; disease state (e.g.,increases in fever), ongoing treatment, etc., and these aspects helpphysicians diagnose a patient. Thus, pulse examination may also behelpful in evaluation of drug response and patient monitoring.

There are various invasive and noninvasive methods to study blood flowthrough arteries. Invasive methods include insertion of a catheter tostudy blood flow in arteries. Noninvasive techniques include dopplerultrasonic probe, pressure sensors, and impedance plethysmography,(e.g., determining changing tissue volumes in the body based on ameasurement of electric impedance at a body surface).

Pulse examination based research currently aims at cardiovascularhealth, identification of risk factors and early detection of diseases.

SUMMARY

In one example aspect, a method of making diagnostic classifications ofpulse signal waveform data is provided that includes receiving pulsesignal waveform data, identifying systolic peak data points in the pulsesignal waveform data corresponding to a systolic peak indicating a heartbeat, and based on the systolic peak data points, identifying complexesin the pulse signal waveform data. A complex comprises a sub-waveform inthe pulse signal waveform data approximately between an on-set datapoint and an off-set data point, and each complex includes deflectiondata points between the on-set point and the off-set point. One of thedeflection data points is a systolic peak corresponding to a heart beat.The method further includes determining a pattern distribution of thecomplexes that indicates a frequency of pulse patterns appearing in thecomplexes of the pulse signal waveform data. The method further includesbased on the pattern distribution, making a diagnostic classification ofthe pulse signal waveform data.

In another example aspect, a computer readable medium having storedtherein instructions executable by a computing device to cause thecomputing device to perform functions is provided. The functions includereceiving, pulse signal waveform data, identifying systolic peak datapoints in the pulse signal waveform data corresponding to a systolicpeak indicating a heart beat, and based on the systolic peak datapoints, identifying complexes in the pulse signal waveform data. Acomplex comprises a sub-waveform in the pulse signal waveform dataapproximately between an on-set data point and an off-set data point,and each complex includes deflection data points between the on-setpoint and the off-set point. One of the deflection data points is asystolic peak corresponding to a heart beat. The method also includesdetermining a pattern distribution of the complexes that indicates afrequency of pulse patterns appearing in the complexes of the pulsesignal waveform data, and based on the pattern distribution, making adiagnostic classification of the pulse signal waveform data.

In still another example aspect, a system is provided that includes adatabase storing known patterns of pulse signals, an input interface forreceiving pulse signal waveform data, and a processor for identifyingsystolic peak data points in the pulse signal waveform datacorresponding to a systolic peak indicating a heart beat. The processorfurther based on the systolic peak data points identifies complexes inthe pulse signal waveform data that comprise a sub-waveform in the pulsesignal waveform data approximately between an on-set data point and anoff-set data point. Each complex includes deflection data points betweenthe on-set point and the off-set point, and one of the deflection datapoints is a systolic peak corresponding to a heart beat. Using thedeflection data points the processor compares a pattern of each complexwith the known patterns of pulse signals in the database, and makes adiagnostic classification of the pulse signal waveform data.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative embodiment of a system for receiving andanalyzing medical data.

FIG. 2 shows an illustrative embodiment of a computing device arrangedfor receiving and analyzing medical data.

FIG. 3 shows a flowchart of an illustrative embodiment of a method foranalyzing pulse signal waveform data to diagnose a disease.

FIG. 4 shows a flowchart of an illustrative embodiment of another methodfor analyzing pulse signal waveform data

FIG. 5 shows an illustrative embodiment of pulse signal waveform data.

FIG. 6-10 show illustrative embodiments of portions of pulse signalwaveform data with data points labeled.

FIGS. 11-22 show illustrative embodiments of portions of pulse signalwaveform data from diabetic patients.

FIGS. 23-28 show illustrative embodiments of portions of pulse signalwaveform data from non-diabetic subjects (e.g., either healthy subjectsor subjects suffering from other diseases).

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

Today's high stress induced lifestyle has raised a burden ofnon-communicable diseases globally, Diabetes mellitus (DM) is ametabolic disease contributing to high mortality and morbidity.According to International Diabetes Federation, incidences of DM in 2025are projected to: rise by 195% in India, for example. Early diagnosisand management can prevent complications, and thus, save untimelydeaths, and failure of vital organs like kidney, retina, heart and bloodvessels, for example.

In example embodiments, systems and methods for diagnosis or predictionof Diabetes Mellitus (DM) are described. Diagnosis or prediction of DMmay be performed by analyzing a pulse, and comparing the pulse with amorphology pattern that has been found to indicate a subject issuffering from DM. In one aspect, a pulse signal can be captured viawrist electrodes using bio-electric sensors based on an impedanceplethysmographic principle, for example, and processed using featureextraction for diagnosis and assessment of the subject's proneness toDM. Analysis of the pulse signal and pulse morphology patterns providesan in-vitro, non-invasive, and low-cost method for diagnosing DM.

In addition, methods and systems in example embodiments can be used toanalyze pulse types for diagnosis of other diseases. For example, arhythm change may be specific to certain diseases (e.g., irregular pulsein atrial fibrillation), and other pulse patterns can be identified asbeing indicative of other diseases.

Referring now to the Figures, FIG. 1 is a system for receiving, andanalyzing medical data. The system includes a medical device 100 thatoutputs to a display device 102. The medical device 100 includes aninput interface 104 that receives medical readings from a patient andsends the medical readings to a processor 106. The medical readings mayinclude pulse signal waveform data of a patient, for example. The pulsesignal waveform data may be received continuously over time, or datarepresenting a pulse signal over time may be received. The inputinterface 104 may also receive inputs from a user including instructionsfor processing the medical readings, for example. The processor 106accesses memory 108 to execute any of the software functions 110 storedtherein, such as to receive the medical readings analyze and process thereadings, and to present data to the display device 102, for example.The processor 106 further accesses the memory 108 to retrieve storedhistorical data 112, or past medical results, personal health/medicaldata including family history data of a patient, anthropometry data of apatient, etc., and may combine the historical data 112 with the receivedmedical readings to process the received medical readings. The processor106 may output results to the display device 102 through an outputinterface 114. A system bus or an equivalent system may also be providedto enable communications between various elements of the medical device100 and the display device 104, for example.

The medical device 100 generally can range from a hand-held device,laptop, or personal computer to a larger computer such as a workstationand multiprocessor. The medical device 100 may also include an inputdevice, such as a keyboard and/or a two or three-button mouse, if sodesired. One skilled in the art of computer systems will understand thatthe example embodiments are not limited to any particular class or modelof computer employed for the medical device 100 and will be able toselect an appropriate system.

The medical device 100 can be attached to a bed/mattress/bedcover torecord pulse recordings when the subject is relaxed, for example, torecord resting heart rate pulse signals. In addition, the inputinterface 104 may be separate from the medical device 100, and mayinclude any standard medical interface for collecting desired medicalreadings from a subject. For example, to collect pulse signals, theinput interface 104 may include or connect to a wrist pulse signaldevice. The input interface 104 may connect to other pulse collectionsignal devices as well, such as those types that connect to other areasof the body like calf, toe, etc.

Further, the input interface 104 and the output interface 114 may be orinclude any standard computer interface and may include, for example, akeyboard, touchscreen display, etc. However, other interfaces may beused as well. Moreover, the memory 108 may include main memory andsecondary storage. The main memory may include random access memory(RAM). Main memory can also include any additional or alternative memorydevice or memory circuitry. Secondary storage can be provided as welland may be persistent long term storage, such as read only memory (ROM),optical or magnetic disks, compact-disc read only memory (CD-ROM), orany other volatile or non-volatile storage systems. The memory 108 mayinclude more software functions 110 as well, for example, executable bythe processor 106 to record signals from a patient and interpret thesignals as medical readings. The software functions 110 may be providedusing machine language instructions or software with object-orientedinstructions, such as the Java programming language. However, otherprogramming languages (such as the C++ programming language forinstance) could be used as well.

The processor 106 may operate according to an operating system, whichmay be any suitable commercially available embedded or disk-basedoperating system, or any proprietary operating, system. The processor106 may comprise one or more smaller central processing units,including, for example, a programmable digital signal processing engine.The processor 106 may also be implemented as a single applicationspecific integrated circuit (ASIC) to improve speed and to economizespace.

It should be further understood that this and other arrangementsdescribed herein are for purposes of example only. As such, thoseskilled in the art will appreciate that other arrangements and otherelements (e.g. machines, interfaces, functions, orders, and groupings offunctions, etc.) can be used instead, and some elements may be omittedaltogether according to the desired results. Further, many of theelements that are described are functional entities that may beimplemented as discrete or distributed components or in conjunction withother components, in any suitable combination and location.

FIG. 2 is a block diagram illustrating another example computing device200 arranged for receiving and analyzing medical data. In a very basicconfiguration 202, computing device 200 typically includes one or moreprocessors 204 and system memory 206. A memory bus 208 can be used forcommunicating between the processor 204 and the system memory 206.

Depending on the desired configuration, processor 204 can be of any typeincluding but not limited to a microprocessor (μP), a microcontroller(μC) a digital signal processor (DSP), or any combination thereof.Processor 204 can include one more, levels of caching, such as a levelone cache 210 and a level two cache 212, a processor core 214, andregisters 216. The processor core 214 can include air arithmetic logicunit (ALU), a floating point unit (FPU), a digital signal processingcore (DSP Core), or any combination thereof. A memory controller 218 canalso be used with the processor 204, or in some implementations thememory controller 218 can be an internal part of the processor 204.

Depending on the desired configuration, the system memory 206 can be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flag memory, etc.) or any combinationthereof. System memory 206 typically includes an operating system 220,one or more applications 222, and program data 224. Application 222includes algorithms 226 that may be arranged to perform any of thefunctions shown in FIGS. 3-4 described below, for example, depending ona configuration of the computing device, 200. Program Data 224 includespulse signal waveform data 228 of a patient, for example. In someexample embodiments, application 222 can be arranged to operate withprogram data 224 on the operating system 220. This described basicconfiguration is illustrated in FIG. 2 by those components within dashedline 202.

Computing device 200 can have additional features or functionality, andadditional interfaces to facilitate communications between the basicconfiguration 202 and any required devices and interfaces. For example,a bus/interface controller 230 can be used to facilitate communicationsbetween the basic configuration 202 and one or more data storage devices232 via a storage interface bus 234. The data storage devices 232 can beremovable storage devices 236, non-removable storage devices 238, or acombination thereof. Examples of removable storage and non-removablestorage devices include magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia can include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data.

System memory 206, removable storage 236 and non-removable storage 238are all examples of computer storage media. Computer storage mediaincludes, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bycomputing device 200. Any such computer storage media can be part ofdevice 200.

Computing device 200 can also include an interface bus 240 forfacilitating communication from various interface devices (e.g., outputinterfaces, peripheral interfaces, and communication interfaces) to thebasic configuration 202 via the bus/interface controller 230. Exampleoutput interfaces 242 include a graphics processing unit 244 and anaudio processing unit 246, which can be configured to communicate tovarious external devices such as a display or speakers via one or moreA/V ports 248. Example peripheral interfaces 250 include a serialinterface controller 252 or a parallel interface controller 254, whichcan be configured to communicate with external devices such as inputdevices (e.g., keyboard, mouse, pen, voice input device, touch inputdevice, etc.) or other peripheral devices (e.g., printer, scanner, etc.)via one or more I/O ports 256. An example communication interface 258includes a network controller 260, which can be arranged to facilitatecommunications with one or more other computing devices 262 over anetwork communication via one or more communication ports 264. Thecommunication connection is one example of a communication media.Communication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, and includes any information delivery media. A“modulated data signal” can be a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can include wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, radio frequency (RF),infrared (IR) and other wireless media. In some examples, the termcomputer readable media as used herein can include storage media,communication media, or both.

Computing device 200 can be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, apersonal data assistant (PDA), a personal media player device, awireless web-watch device, a personal headset device, an applicationspecific device, or a hybrid device that include any of the abovefunctions. Computing device 200 can also be implemented as a personalcomputer including both laptop computer and non-laptop computerconfigurations.

The medical device 100 in FIG. 1 or the computing device 200 in FIG. 2may be configured to operate to receive a pulse signal and analyze thepulse signal to diagnose a disease, such as Diabetes Mellitus (DM).Diagnosis of DM may be performed by comparing the pulse signal with amorphology pattern that has been found to indicate a subject issuffering from DM, for example.

FIG. 3 shows a flowchart of an illustrative embodiment of a method 200for analyzing pulse signal waveform data to diagnose a disease. Itshould be understood that for this and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer readable medium, forexample, such as a storage device including a disk or hard drive. Inaddition, each block may represent circuitry that is wired to performthe specific logical functions in the process. Alternativeimplementations are included within the scope of the example embodimentsof the present disclosure in which functions may be executed out oforder from that shown or discussed, including substantially concurrentor in reverse order, depending on the functionality involved, as wouldbe understood by those reasonably skilled in the art.

Initially, patient data is collected at block 302. Patient data mayinclude test results of various clinical investigations includingbiochemistry (e.g., sugar), hematology, electrocardiogram, angiogram,lipids, x-ray and other relevant investigations, for example. Inaddition, patient data may include personal health or medical data of apatient, such as the patient's personal information, risk factors suchas family history from maternal and paternal parents for diseases likeDiabetes Mellitus, hypertension, ischaemic heart disease, or othermetabolic diseases or conditions. Patient data may also include observedsigns and symptoms of the patient or anthropometric data, such asheight, weight, body mass index (BMI), and waist hip ratio, for example.

Following, pulse signal waveform data is collected at block 304. Pulsesignal waveform data may be collected by recording Impedancephlebography, or impedance plethysmography (IPG) based pulse signal dataas a function of time. The IPG-based pulse signal data may indicatesmall changes in electrical resistance of regions of the body thatreflect blood volume changes. For example, pulse signals may be acquiredor captured from a subjects wrist using bio-electric sensors. The pulsesignals may be impedance plethysmographic pulse signals captured byusing the bio-electric sensors. The collected pulse signals indicate arate of change of impedance in a body segment, such as the wrist of thesubject. Further, pulse signals may be collected over a period of time,such as for about five minutes, and may be configured into the pulsesignal waveform data. The pulse signal waveform data may be periodic orsubstantially periodic, for example.

Pulse signals may be recorded over any period of time. In some examples,to obtain a number of patterns of pulse signal waveform data, arecording interval can be at least about one minute. Also, because thepulse signal waveform data is periodic, a long interval may not provideany additional information. An interval of about five minutes has beenfound to provide an effective and reliable amount of data. The intervalmay be reduced to about four minutes in good conditions (e.g., lownoise, low disturbances) for some examples. However, the method 300 maybe performed for pulse signal waveform data collected over any timeinterval. Results may be more accurate and reliable for an interval ofabout five minutes, for example.

Next, at block 306, the pulse signal waveform data is processed. In oneexample, the pulse signal waveform data can be further processed andconverted to an ASCII format, for example. The pulse signal waveformdata may be digitized to a binary format, which can then be decomposedinto time series data that is stored in ASCII format. To do so, thebinary data is input, and a first four bytes of data may represent asample count. The sample count indicates, for example, a number of datapoints or records captured. Data can be stored in a sequence of twobytes, which are converted into a long number (e.g., a decimal number)that represents impedance at a time instance. Similarly, impedance canbe calculated from the several sets of two bytes, and a rate of changeof impedance can be determined and output over time as values separatedby commas in a text file, for example, to output a pulse time seriesdata file in ASCII format.

In another example, the pulse signal waveform data may be furtherprocessed to remove signal noise. For example, band-pass filtering isused for filtering undesired frequencies of the pulse signal waveformdata to reduce background noise and optimize a signal-to-noise ratio. Toremove signal noise, the pulse time series data file in ASCII format canbe used. For a pulse signal waveform data collected over about fiveminutes, and sampled at a frequency of about 100 Hz, about 30,000 datapoints will be collected. A discrete Fourier transform can be performedon the 30,000 data points by applying a Fast Fourier Transform (FFT)algorithm to the data to decompose the data points into components ofdifferent frequencies, for example. Next, a power spectral density ofthe FFT decomposed data points can be, obtained, and frequencies atwhich a high signal power or high intensity is concentrated can beidentified. A threshold value reflecting low signal power or low energycan be determined. A threshold filtering technique can be applied to theFFT decomposed data points to set FFT coefficients at frequencies thathave less energy than the threshold value to zero. An inverse PET (iFFT)can be applied to the modified PET coefficients to provide a de-noisedpulse signal waveform data.

Following, at block 308, features of the pulse signal waveform data areidentified. Since the input data of the pulse signal waveform data mayinclude about 30,000 data points, and because the pulse signal waveformdata is substantially periodic, the data points may include repetitiveor redundant information. Thus, the data points can be transformed intoa reduced representation set of features that retain useful information,and redundant information can be discarded.

Because the pulse signal waveform data is substantially periodic, thepulse signal waveform data includes sub-waveforms or complexes. Toidentify features of the pulse signal waveform data, the complexes areextracted that are defined or identified by an on-set data point and anoff-set data point of a corresponding sub-waveform in the pulse signalwaveform data. Each complex may include a data point between the on-setand off-set data points that is a systolic peak corresponding to a heartbeat. Improper peaks may be seen in the pulse waveform signal data,which can result in ectopic complexes. As a normal range for a restedheart rate is about 60-90 beats per minute, abnormal peaks occurringoutside of a predetermined heart beat range can be interpreted as apathology or ignored if the peaks are found as noise or interpreted asnoise. In normal operation, few or no multiple, high amplitude peakswill be clustered together. However, in some instances, a peak may occurtoo soon after a previous peak (based on normal resting heart rates), ora peak may be missing in the instance of a missed heart beat or missedrecording of the heart beat. For consideration of a pulse morphology,peaks occurring outside of an expected range, or missed peaks, can beignored, but may be noted for instances of bradycardia or tachycardiaconditions, for example.

Other features of each complex may be identified as well including datapoints preceding and following the systolic peak so as to characterizethe complex. In addition, other features of data points may beidentified, such as amplitude of a data point in the pulse signalwaveform data, intervals between data points, and correlation among thedata points. Mathematical relations (e.g., equal to, greater than, lessthan, etc.) between pairs of data points may also be obtained tocharacterize the pulse signal waveform data associated with eachcomplex. Still further, other algebraic relationships amongst the datapoints can be obtained to uniquely characterize a pattern associatedwith each complex, for example.

Next, the identified features can be compared to known patterns of pulsesignal waveform data and a pulse morphology is output, at block 310. Forexample, a database may include a number of known patterns of pulsesignals in pulse signal waveform data, and corresponding features foreach of the known patterns. The identified features of the pulse signalwaveform data can be compared to the corresponding features for each ofthe known patterns to identify matches. Depending on a number offeatures being compared, a number of matches may be identified. If anacceptable number of matches is found (e.g., the features can beapproximately mapped onto an already existing pattern from thedatabase), the pulse signal waveform data may be labeled by a label ofthe corresponding known pattern. If less than an acceptable number ofmatches is found, the pulse signal waveform data may be stored in thedatabase as a new pattern with a new label. Data including the labels ofpatterns is output as the pulse morphology of the pulse signal waveformdata.

Thus, a database of distinct pulse patterns observed in pulse waveformsignal data can be constructed by populating the database with newpatterns found in pulse waveform signal data, and features of the newpatterns are given unique labels.

An acceptable number of matches may depend on the number of featuresbeing, compared, and may be, for example, at least about a 50% to abouta 75% or higher match rate. In addition, matches of features may occurif data points defining the features have magnitudes approximately thesame or within a predetermined tolerance of each other. Alternatively,absolute values of the deflection points may not be compared, butrather, relations (e.g., less than, equal to, greater than) of thevalues of the deflection points are observed. Furthermore, becausefeatures of the pulse signal waveform data are seen over time, timing ofthe features or between the features may be matched as well with timingof features in the known patterns. To match a timing feature, the timingof a feature may be approximately the same or within a predeterminedtolerance of each other.

In an alternate embodiment, instead of comparing the identified featuresto known patterns of pulse signal waveform data at block 310, theidentified features of each complex may be used to construct a databaseof patterns as the patterns are observed in the pulse signal waveformdata. A pattern is uniquely characterized by mathematical relationsamongst features of data points, e.g., amplitudes of the data points.Once the database becomes populated with patterns, syntactical patternrecognition techniques can be used for pattern matching because there isa clear structure observed in the pulse morphology; for example.

Following, at block 312, a frequency distribution of patterns in thepulse morphology is determined. For example, a number of times eachdifferent pattern appears in the pulse morphology is determined. A tablemay be prepared including a number of times each different patternappears in the pulse morphology.

A diagnostic classification of the pulse signal waveform data is thenprovided at block 314. Pulse signals captured during an interval of fiveminutes may display different morphology patterns corresponding todifferent complexes. For diagnostic classification, a most dominantpattern appearing in the pulse morphology over the specified interval(e.g., the pattern appearing with the most frequency) can be identified,and a label of the most dominant pattern is determined. The label mayindicate a specific disease that is characterized by a patient that hasa specific pulse signal waveform data.

Alternatively, after identifying the most dominant pattern, the mostdominant pattern may be compared with pulse patterns classified as beingindicative of a specific disease. In one example, the most dominantpattern may be compared with pulse patterns classified as beingindicative of Diabetes Mellitus or Metabolic Syndrome (e.g., acombination of medical disorders that increase a risk of developingcardiovascular disease and diabetes).

In addition, because impedance plethysmography is a technique fordetecting blood vessel occlusion that determines volumetric changes inthe limb, other disorders or diseases affecting vascular/cardiovascularhealth can be diagnosed using the method 300. Example disorders that maybe diagnosed using the method 300 include cardiovascular heart disease,ischaemic heart disease, atherosclerosis, angina, stroke,cerebrovascular disease, congestive heart failure, coronary arterydisease, myocardial infarction, or peripheral vascular disease. Todiagnose any of these diseases a particular pulse pattern characterizingor being indicative of the disease is established and compared with themost dominant pattern appearing in the pulse signal waveform data. Togenerate or identify characterizing patterns, blind, observationalstudies are performed involving, healthy subjects as well as patientssuffering from the earmarked disease and patients suffering from otherdiseases, for example.

FIG. 4 shows a flowchart of an illustrative embodiment of another method400 for analyzing pulse signal waveform data, and FIG. 5 show an examplepulse signal waveform data. Initially, pulse signal waveform data iscollected at block 402. For example, a series of pulse signal waveform,data may be collected over time. Following, the pulse signal waveformdata is characterized into windows of data points at block 404. Thewindows may be referred to as complexes. The pulse signal waveform datamay be collected over a time period of about five minutes, and data maybe collected at a sampling frequency of about 100 Hz to collect a totalof about 30,000 data points. A window may include about 100 data pointsor be equivalent to about one second, for example. A size of a windowmay be chosen based on a patient's resting heart rate. For example, anormal lower boundary value of a resting heart rate is about 60 beatsper minute, or about one beat per second. Thus, within a window size ofone second, the heart will beat about once. Thus, within each complex,about one systolic peak will appear.

In the pulse signal waveform data shown in the graph of FIG. 5, the datais graphed over about 4 seconds of time during which the heart beat isseen about 4 times giving rise to 4 systolic peak data points. Thus,next at block 406, systolic peaks are identified within each window byfinding data points with maximum amplitudes in the windows of about 100data points over the entire pulse signal waveform data of about a 300second interval, for example. The data points representing the systolicpeaks are given a unique label, such as “C” in FIG. 5. Spurious peaks inthe complexes that would indicate a resting heart rate beyond the rangeof about 60 to about 100 beats per minute can be ignored, for example.

Following, deflection data points surrounding the systolic peak datapoints are identified. For example, a first previous data pointpreceding the systolic peak data point that has a local minimumamplitude is identified at block 408. The first previous data point isgiven a label, such as “A” in FIG. 5. The first preceding data point maybe referred to as the on-set data point referring to the on-set of asystolic peak. As shown in FIG. 5, to identify the on-set data point“A”, an algorithm is performed to identify a local minimum prior to thesystolic peak “C” data point. Amplitudes of data points preceding thesystolic peak “C” are compared to one another until the local minimum isidentified (e.g., until a rate of change of the line graph begins tochange). Or, for example, amplitudes of data points are compared until achange is found that would indicate a local minimum (such as a changefrom a decreasing amplitude to an increasing amplitude in neighboringdata points).

Following, deflection points following or subsequent to the systolicpeak C are identified. For example, a next deflection point isidentified by finding a first subsequent data point following thesystolic peak data point that has a local minimum amplitude at block410. The first subsequent data point is given a label, such as “X” inFIG. 5.

Next, a second subsequent data point following the first subsequent datapoint that has a local maximum amplitude is identified at block 412. Thesecond subsequent data point is given, a label, such as “Y” in FIG. 5.To find a local maximum, similar algorithms are performed as to find alocal minimum, such as to compare amplitudes of data points until achange is found that would indicate a local maximum (such as a changefrom an increasing amplitude to a decreasing amplitude in neighboringdata points).

Next, a third subsequent data point following the second subsequent datapoint that has a local minimum amplitude is identified at block 414. Thethird subsequent data point is given a label, such as Y′ in FIG. 5.Next, a fourth subsequent data point following the third subsequent datapoint that has a local maximum amplitude is identified at block 416. Thefourth subsequent data point is given a label, such as “O” in FIG. 5.Last, a fifth subsequent data point following the fourth subsequent datapoint that has a local minimum amplitude is identified at block 418. Thefifth subsequent data point is given a label, such “Z” in FIG. 5. Thedata point Z is referred to as the off-set data point indicating an endof the window, for example.

As seen in FIG. 5, a sub-waveform from data points A to Z is referred toas complex. Since the pulse signal waveform data is periodic, thesecomplexes repeat throughout the five minute recording interval, forexample. Data points A are the on-set data points and data points Z arethe off-set data points of the sub-waveforms in the various complexes,and data points A, C, X, Y, Y′, O and Z are the deflection points ineach of the complexes.

In example embodiments, using the methods of FIGS. 3 and 4, pulsemorphology pattern recognition can be performed, and a diagnosticclassification can be provided to indicate if a subject suffers from adisease. Data regarding physiological variables, clinicalinvestigations, and family history, etc., can also be captured forvalidation of the diagnostic classification. The methods 300 and 400 fordiagnostic classification are in-vitro, non-invasive and low-cost, forexample.

Experimental studies were performed to evaluate the methods 300 and 400.Data was collected at multiple centers using healthy volunteers,diabetic patients, and non-diabetic patients that suffered from othermetabolic diseases, such as ischemic heart disease (IHD). Tables 1 and 2below indicate the different centers, patients, and correspondingdiseases of the patients. Note that a patient may have been sufferingfrom more than one disease/disorder. Hence, although a total number ofpatients studied was 316, the total number of eases studied was 394.

TABLE 1 Center-wise Cases No. of Centers Volunteers Volunteers HospitalA Patients 106 Hospital B Patients 71 Clinic Patients 74 FoundationPatients 37 Diabetic Camp Patients 28 College of Arts & Science Healthy53 Engineering College Healthy 46 CDAC Healthy 81 Others (Health Dept.,etc.) Healthy 61 Total 557

TABLE 2 Disease-wise Cases No. of Disease patients IHD 87 Dislipidemia +Other Heart 21 Diseases Hypertension 82 Diabetes Mellitus 91 Asthma &Other Respiratory 22 Diseases Arthritis + Musculoskeletal 27Gastroenterological 43 Nephrological 7 Neurological 6 Cancer 4 Anaemia 4Total 394

Initially, for each subject, multidimensional data was collected. Thedimensions of the data included 1) medical family history from maternaland paternal sides, 2) personal health/medical record including sex andage data, 3) clinical investigations of the subject such as bloodpressure, glucose level (fasting & PP), lipid profile, and hemogram, 4)pulse related parameters such as strength, volume, depth, pulse rate,etc., 5) anthropometry data such, as height and weight, and 6) impedanceplethysmographic pulse signal recorded for a period of about 300 secondsat a sampling frequency of about 100 Hz.

Clinical data was procured and high frequency noise was filtered. Thedata was processed in a series of steps including those described abovein FIGS. 3 and 4. For example, the processing included (1) signalacquisition along with filtering, (2) data conversion from proprietarybinary format to ASCII format, (3) data transformation for furtherprocessing including identifying various deflection points, complexes,and classification of the complexes into “dominant” and “nondominant”(ectopic) types, (4) pulse waveform recognition to identify onset andoffset of diagnostic waves and major deflection points, (5) featureextraction measuring amplitudes and intervals, and (6) diagnosticclassification.

The pulse signal waveform data is captured and studied in the timedomain. Each complex is identified by the on-set data points in thepulse signal waveform data. In each complex, seven major deflectionpoints (A, C, X, Y, Y′, O, Z) are identified using the method 400described above. FIGS. 6-9 illustrate example pulse morphology patternsthat were generally observed. Each pattern is uniquely recognized bycorrelations amongst the deflection points of the pulse waveform.

For example, in FIG. 6, a complex is shown that has values of deflectionpoints that substantially follow the relationships shown in Equations(1) and (2) below.

$\begin{matrix}{Y^{\prime} > A > X} & {{Equation}\mspace{14mu} (1)} \\{{\left( {A - X} \right) < \left( \frac{\alpha \; X}{100} \right)},{{where}\mspace{14mu} \alpha \mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {constant}}} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

An example value of α is about 80. For example, a value of thedeflection point A may have a value greater than the value of thedeflection point X, but the difference between the two values should notbe too large.

The complex shown in FIG. 7 has values of deflection points thatsubstantially follow the relationship shown in Equation (3).

Y′<A<X  Equation (3)

The complex shown in FIG. 8 has values of deflection points thatsubstantially follow the relationships shown in Equations (4) and (5).

$\begin{matrix}{X < {\min \left\{ {A,Y^{\prime}} \right\}}} & {{Equation}\mspace{14mu} (4)} \\{{\left( {A - X} \right) > \left( \frac{\alpha \; X}{100} \right)},{{where}\mspace{14mu} \alpha \mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {constant}}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

The complex shown in FIG. 9 has values of deflection points thatsubstantially follow the relationship shown in Equation (6).

X=Y′=Z  Equation (6).

The patterns shown in FIGS. 6-9 may, be stored in a database and usedduring pulse morphology creation or generation (e.g., in method 300).

FIG. 10 illustrates an example complex that has values of deflectionpoints that substantially follow the relationship shown in Equation (7).

A≧X>Y′  Equation (7)

The complex shown in FIG. 10, including deflections points that followthe relationship in Equation (7) has been classified to be indicative ofdiabetes mellitus (DM).

In each of Equations (1)-(7), the relationships may still be consideredsatisfied if the values of the deflection data points substantiallyfollow the relationships in the Equations within a tolerance level. Forexample, Equation (7) may be considered to be satisfied if X is greaterthan A by the tolerance level.

Accuracy of the pulse morphology pattern classified to be indicative ofDM was about 98.74%. Dominance of a pulse morphology pattern as shown inFIG. 10 in the pulse morphology of the Impedance Plethysmographic pulsesignal waveform data captured over a period of about five minutes wasused as an identifier to diagnose a subject as diabetic. Table 3 belowsummarizes results of the experimental study.

TABLE 3 Health Status No. of Volunteers Exceptions Found Remarks Healthy241 2 Of 241, only 2 Patients: 316 5 healthy subjects Diabetic 91 had apulse pattern Non-diabetic 225 indicating DM. Of Total 557 7 225non-diabetic patients, only 5 subjects had pulse pattern indicating DM.Accuracy of the hypothesis: 98.7423%

FIGS. 11-22 illustrate pulse signal waveform, data from diabeticpatients. In FIG. 11, the patient had blood sugar levels of (fasting)F—118 and (postprandial) PP—130. In FIG. 12, the patient had blood sugarlevels of F—130 and PP—148. In FIG. 13, the patient had blood sugarlevels of F—137 and PP—187. In FIG. 14, the patient had blood sugarlevels of F—134 and PP—287. In FIG. 15, the patient had blood sugarlevels of F—130 and PP—210. In FIG. 16, the patient had blood sugarlevels of F—134 and PP—200. In FIG. 17, the patient had blood sugarlevels of F—144 and PP—200.

As seen in FIGS. 11-22, each of the pulse signal waveform data satisfiesthe relationship (A≧X>Y′) of Equation (7). In FIGS. 17-18, however, X isslightly greater than A, but within an acceptable tolerance level, forexample.

FIGS. 23-28 illustrate pulse signal, waveform data from non-diabeticsubjects (e.g., either healthy patients or patients suffering from otherdiseases). In FIGS. 23-28, the pulse signal waveform data generally doesnot satisfy the relationship (A≧X>Y′) of Equation (7).

Hyperglycemia induced vascular changes in diabetic patients can lead toseveral complications such as retinopathy, nephropathy, andneuropathies. Using example methods described herein, pulse examinationand classification of an impedance plethysmographic pulse morphologypattern for DM can enable early detection of DM or other hyperglycemiainduced vascular changes.

In example embodiments, methods may be performed by patients at home andminimize a need for time consuming visits to pathological laboratories,for example. Early detection of diseases such as DM can enable earlycorrective measures in diet and lifestyle and proper treatment forreducing morbidity and mortality caused directly or indirectly by DM,for example.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

The present disclosure is not to be limited in terms of the particularembodiments described in this disclosure, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. Such modifications and variations are intendedto fall within the scope of the appended claims. The present disclosureis to be limited only by the terms of the appended claims, along withthe full scope of equivalents to which such claims are entitled. It isto be understood that this disclosure is not limited to particularmethods, reagents, compounds compositions or biological systems, whichcan, of course, vary. It is also to be understood that the terminologyused herein is for the purpose of describing particular embodimentsonly, and is not intended to be limiting.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations. In addition, even if a specificnumber of an introduced claim recitation is explicitly recited, thoseskilled in the art will recognize that such recitation should beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,etc.” is used, in general such a construction is intended in the senseone having skill in the art would understand the convention (e.g., “asystem having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether; A and C together, B and C together, and/or A, B, and Ctogether, etc.). In those instances where a convention analogous to “atleast one of A, B, or C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, or C” wouldinclude but not be limited to systems that have A alone, B alone, Calone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). It will be further understood by those withinthe art that virtually any disjunctive word and/or phrase presenting twoor more alternative terms, whether in the description, claims, ordrawings, should be understood to contemplate the possibilities ofincluding one of the terms, either of the terms, or both terms. Forexample, the phrase “A or B” will be understood to include thepossibilities of “A” or “B” or “A and B.”

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualMember or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languagesuch as “up to,” “at least,” “greater than,” “less than,” and the likeinclude the number recited and refer to ranges which can be subsequentlybroken down into subranges as discussed above. Finally, as will beunderstood by one skilled in the art, a range includes each individualmember. Thus, for example, a group having 1-1 cells refers to groupshaving 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers togroups having 1, 2, 3, 4, or 5 cells, and so forth.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

1. A method of making diagnostic classifications of pulse signalwaveform data, the method comprising: receiving pulse signal waveformdata; identifying systolic peak data points in the pulse signal waveformdata corresponding to a systolic peak indicating a heart beat; based onthe systolic peak data points, identifying complexes in the pulse signalwaveform data, wherein a complex comprises a sub-waveform in the pulsesignal waveform data approximately between an on-set data point and anoff-set data point, wherein the on-set data point is a data pointpreceding a given systolic peak data point that has a local minimumamplitude and the off-set data point is a data point subsequent to thegiven systolic peak that has a local minimum amplitude, and wherein eachcomplex includes deflection data points between the on-set point and theoff-set point, wherein one of the deflection data points is a systolicpeak corresponding to a heart beat; determining a pattern distributionof the complexes based on relative magnitudes of at least threedeflection data points of the complexes, wherein the patterndistribution indicates a frequency of pulse patterns appearing in thecomplexes of the pulse signal waveform data; and based on the patterndistribution, making a diagnostic classification of the pulse signalwaveform data.
 2. The method of claim 1, wherein making the diagnosticclassification of the pulse signal waveform data comprises determiningthat the pulse signal waveform data is indicative of Diabetes Mellitusor Hyperglycemia induced vascular changes.
 3. The method of claim 1,further comprising for each complex determining the deflection datapoints by: identifying a first previous data point preceding thesystolic peak data point that has a local minimum amplitude, the firstpreceding data point being the on-set data point; identifying a firstsubsequent data point following the systolic peak data point that has alocal minimum amplitude; identifying a second subsequent data pointfollowing the first subsequent data point that has a local maximumamplitude; identifying a third subsequent data point following thesecond subsequent data point that has a local minimum amplitude;identifying a fourth subsequent data point following the thirdsubsequent data point that has a local maximum amplitude; andidentifying a fifth subsequent data point following the fourthsubsequent data point that has a local minimum amplitude, the fifthsubsequent data point being the off-set data point.
 4. The method ofclaim 3, wherein making the diagnostic classification of the pulsesignal waveform data comprises determining that the pulse signalwaveform data is indicative of Diabetes Mellitus when the followingrelationship is substantially satisfied:A≧X>Y′, where A is the first previous data point, X is the firstsubsequent data point, and Y′ is the third subsequent data point.
 5. Themethod of claim 1, further comprising receiving personal health andmedical data of a patient from which the pulse signal waveform data wasrecorded, and based on the personal health and medical data of thepatient, making the diagnostic classification of the pulse signalwaveform data.
 6. The method of claim 1, further comprising convertingthe pulse signal waveform data into time series data that is stored inASCII format.
 7. The method of claim 6, further comprising filtering thepulse signal waveform data by: performing a discrete Fourier transformon the time series data to decompose data points into components ofdifferent frequencies; determining a power spectral density of thedecomposed data points; identifying frequencies at which a signal poweris concentrated; based on the identified frequencies, determining athreshold value reflecting low signal power; setting decomposed datapoints at frequencies that have less signal power than the thresholdvalue to zero to provide modified data points; and performing an inversefast Fourier transform (iFFT) to the modified data points to outputde-noised pulse signal waveform data.
 8. The method of claim 1, furthercomprising identifying systolic peaks corresponding to heart beats ineach complex by identifying a data point in each complex having amaximum amplitude.
 9. The method of claim 1, further comprising based onrelationships between the deflection data points, characterizing eachcomplex in the pulse signal waveform data.
 10. The method of claim 1,further comprising identifying within the pattern distribution ofcomplexes a pattern appearing most frequently in the pulse signalwaveform data.
 11. The method of claim 10, further comprising comparingthe pattern appearing most frequently in the pulse signal waveform datawith a pattern classified as being indicative of a disease.
 12. Themethod of claim 1, further comprising comparing deflection data pointsof patterns of complexes within the pattern distribution of complexeswith deflection data points in known patterns of pulse signals toidentify matches.
 13. The method of claim 12, further comprising whendeflection data points of a given pattern of a complex do notsufficiently match with deflection data points in any of the knownpatterns of pulse signals, populating a database of distinct pulsesignal patterns observed in pulse waveform signal data with the givenpattern.
 14. The method of claim 1, wherein receiving pulse signalwaveform data comprises receiving Impedance phlebography/impedanceplethysmography based pulse signal data as a function of time.
 15. Themethod of claim 1, wherein receiving pulse signal waveform datacomprises receiving pulse signal waveform data collected over a timeperiod, and wherein a time interval of the sub-waveform in the pulsesignal waveform data is selected based on a patient's resting heart rateso that approximately one systolic peak indicating the heart beat occursduring the time interval of the sub-waveform.
 16. A computer readablemedium having stored therein instructions executable by a computingdevice to cause the computing device to perform functions of: receivingpulse signal waveform data; identifying systolic peak data points in thepulse signal waveform data corresponding to a systolic peak indicating aheart beat; based on the systolic peak data points, identifyingcomplexes in the pulse signal waveform data, wherein a complex comprisesa sub-waveform in the pulse signal waveform data approximately betweenan on-set data point and an off-set data point, wherein the on-set datapoint is a data point preceding a given systolic peak data point thathas a local minimum amplitude and the off-set data point is a data pointsubsequent to the given systolic peak that has a local minimumamplitude, and wherein each complex includes deflection data pointsbetween the on-set point and the off-set point, wherein one of thedeflection data points is a systolic peak corresponding to a heart beat;determining a pattern distribution of the complexes based on relativemagnitudes of at least three deflection data points of the complexes,wherein the pattern distribution indicates a frequency of pulse patternsappearing in the complexes of the pulse signal waveform data; and basedon the pattern distribution, making a diagnostic classification of thepulse signal waveform data.
 17. The computer readable medium of claim16, wherein the functions further include determining that the pulsesignal waveform data is indicative of Diabetes Mellitus.
 18. Thecomputer readable medium of claim 16, wherein the functions furtherinclude for each complex determining the deflection data points by:identifying a first previous data point preceding the systolic peak datapoint that has a local minimum amplitude, the first preceding data pointbeing the on-set data point; identifying a first subsequent data pointfollowing the systolic peak data point that has a local minimumamplitude; identifying a second subsequent data point following thefirst subsequent data point that has a local maximum amplitude;identifying a third subsequent data point following the secondsubsequent data point that has a local minimum amplitude; identifying afourth subsequent data point following the third subsequent data pointthat has a local maximum amplitude; and identifying a fifth subsequentdata point following the fourth subsequent data point that has a localminimum amplitude, the fifth subsequent data point being the off-setdata point, wherein making the diagnostic classification of the pulsesignal waveform data comprises determining that the pulse signalwaveform data is indicative of Diabetes Mellitus when the followingrelationship is substantially satisfied:A≧X>Y′, where A is the first previous data point, X is the firstsubsequent data point, and Y′ is the third subsequent data point. 19.The computer readable medium of claim 16, wherein the functions furtherinclude: identifying within the pattern distribution of complexes apattern appearing most frequently in the pulse signal waveform data; andcomparing the pattern appearing most frequently in the pulse signalwaveform data with a pattern classified as being indicative of adisease.
 20. A system comprising: a database storing known patterns ofpulse signals; an input interface for receiving pulse signal waveformdata; and a processor configured to identify systolic peak data pointsin the pulse signal waveform data corresponding to a systolic peakindicating a heart beat, the processor further based on the systolicpeak data points configured to identify complexes in the pulse signalwaveform data, wherein a complex comprises a sub-waveform in the pulsesignal waveform data approximately between an on-set data point and anoff-set data point, wherein the on-set data point is a data pointpreceding a given systolic peak data point that has a local minimumamplitude and the off-set data point is a data point subsequent to thegiven systolic peak that has a local minimum amplitude, and wherein eachcomplex includes deflection data points between the on-set point and theoff-set point, wherein one of the deflection data points is a systolicpeak corresponding to a heart beat, wherein based on relative magnitudesof at least three deflection data points of the complexes the processoris configured to compare a pattern of each complex with the knownpatterns of pulse signals in the database, and to make a diagnosticclassification of the pulse signal waveform data.